基于残差修正的含小水电网供负荷预测研究  

Research on power supply and load forecasting of small water grid based on residual error correction

作　　者：胡尧 李黄强 舒征宇 姚钦 李世春[1] 许布哲 Hu Yao;Li Huangqiang;Shu Zhengyu;Yao Qin;Li Shichun;Xu Buzhe(College of Electrical Engineering&New Energy,Three Gorges University,Yichang 443000,China;State Grid Hubei Electric Power Co.,Yichang 443000,China)

机构地区：[1]三峡大学电气与新能源学院,宜昌443000 [2]国网湖北省电力有限公司宜昌供电公司,宜昌443000

出　　处：《电子测量技术》2022年第6期50-58,共9页Electronic Measurement Technology

基　　金：国家自然科学基金(51907104)项目资助。

摘　　要：针对小水电高渗透率地区网供负荷预测准确率较低的问题,提出一种基于人工智能和残差修正的网供负荷预测模型,对蕴含在网供负荷中的周期分量和随机分量进行预测和结果修正。采用集合经验模态分解(EEMD)提取网供负荷中不同频段的分量,构建基于模态分量的多层次门控循环单元(GRU)网络模型,通过提升网络模型的复杂程度提高测试集上预测结果的准确率。此外,引入费歇值表征降雨对小水电出力的累积效应影响,在预测结果输出环节加入费歇信息加权的马尔科夫(FI-WMC)残差修正步骤,降低小水电出力不确定性导致的预测结果偏差。仿真验证的结果表明,多层级EEMD-GRU-FIWMC模型可以更好地适用于小水电高渗透率地区的网供负荷预测,在小水电渗透率为20%以上的地区,相对于传统的GRU模型和无残差修正模型,其预测准确率分别提升7.61%、3.85%。Aiming at the problem of the low accuracy of grid supply load forecasting in high permeability areas of small hydropower, a grid supply load forecasting model based on artificial intelligence and residual correction is proposed to predict and correct the periodic and random components contained in the grid supply load. Ensemble empirical modal decomposition(EEMD) is used to decompose and extract the components of different frequency bands in the network load, and a multi-level gated recurrent unit(GRU) network model based on modal components is constructed, the accuracy of the prediction results on the test set is improved by increasing the complexity of the network model. In addition, the Fischer value is introduced to characterize the cumulative effect of rainfall on the output of small hydropower, and the fisher information-weighted Markov chain(FI-WMC) residual correction step is added in the output of prediction results, reduce the deviation of prediction result caused by the uncertainty of small hydropower output. The results of simulation verification show that the multi-level EEMD-GRU-FIWMC model can be better applied to the grid load forecasting in areas with high permeability of small hydropower. In areas where the penetration rate of small hydropower is above 20%, compared with the traditional GRU model and the no-residual correction model, its prediction accuracy is increased by 7.61% and 3.85%, respectively.

关 键 词：网供负荷 小水电 集合经验模态分解 门控循环单元 费歇信息 马尔科夫链 

分 类 号：TM715[电气工程—电力系统及自动化]